Using deep neural networks to detect complex spikes of cerebellar Purkinje cells
Akshay Markanday, Joachim Bellet, Marie E. Bellet, Junya Inoue, Ziad M. Hafed, Peter Thier
Abstract
Purkinje cell "complex spikes", fired at perplexingly low rates, play a crucial role in cerebellum-based motor learning. Careful interpretations of these spikes require manually detecting them, since conventional online or offline spike sorting algorithms are optimized for classifying much simpler waveform morphologies. We present a novel deep learning approach for identifying complex spikes, which also measures additional relevant neurophysiological features, with an accuracy level matching that of human experts yet with very little time expenditure.
Topics & Concepts
Spike (software development)NeuroscienceComputer sciencePurkinje cellPostsynaptic potentialExcitatory postsynaptic potentialArtificial intelligenceDendritic spikeCerebellumPattern recognition (psychology)Inhibitory postsynaptic potentialChemistryPsychologyReceptorBiochemistrySoftware engineeringVestibular and auditory disordersNeural dynamics and brain functionNeonatal and fetal brain pathology